Update README.md

README.md

@@ -113,7 +113,7 @@ Provide the sizes of each split. As appropriate, provide any descriptive statist
 | Input Sentences         |   225892925   |   11083851   |
 | Average Sentence Length |   61    |   25   |
 
-Below you can see the histogram of word/paragraph over the two splits of the dataset.
+Below you can see the log-based histogram of word/paragraph over the two splits of the dataset.
 
 <div align="center">
 <img src="https://huggingface.co/datasets/SLPL/naab/resolve/main/naab-hist.png">
@@ -125,22 +125,40 @@ Below you can see the histogram of word/paragraph over the two splits of the dat
 
 Due to the lack of a huge amount of text data in lower resource languages - like Farsi - researchers working on these languages were always finding it hard to start to fine-tune such models. This phenomenon can lead to a situation in which the golden opportunity for fine-tuning models is just in hands of a few companies or countries which contributes to the weakening the open science.
 
-The last biggest cleaned merged textual corpus in Farsi is a 70GB cleaned text corpus from a compilation of 8 big data sets that have been cleaned and can be downloaded directly. Our solution to the discussed issues is called naab. It provides 126GB (including more than 224 million sequences and nearly 15 billion words) as the training corpus and 2.3GB (including nearly 11 million sequences and nearly 300 million words) as the test corpus.
+The last biggest cleaned merged textual corpus in Farsi is a 70GB cleaned text corpus from a compilation of 8 big data sets that have been cleaned and can be downloaded directly. Our solution to the discussed issues is called naab. It provides **126GB** (including more than **224 million** sequences and nearly **15 billion** words) as the training corpus and **2.3GB** (including nearly **11 million** sequences and nearly **300 million** words) as the test corpus.
 
 ### Source Data
+
+The textual corpora that we used as our source data are illustrated in the figure below. It contains 5 corpora which are linked in the coming sections.
+
 <div align="center">
 <img src="https://huggingface.co/datasets/SLPL/naab/resolve/main/naab-pie.png">
 </div>
 
-#### Persian NLP
+#### [Persian NLP](https://github.com/persiannlp/persian-raw-text)
+
+This corpus includes eight corpora that are sorted based on their volume as below:
 
+ - [Common Crawl](https://commoncrawl.org/): 65GB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/commoncrawl_fa_merged.txt))
+ - [MirasText](https://github.com/miras-tech/MirasText): 12G 
+ - [W2C – Web to Corpus](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0022-6133-9): 1GB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/w2c_merged.txt))
+ - Persian Wikipedia (March 2020 dump): 787MB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/fawiki_merged.txt))
+ - [Leipzig Corpora](https://corpora.uni-leipzig.de/): 424M ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/LeipzigCorpus.txt))
+ - [VOA corpus](https://jon.dehdari.org/corpora/): 66MB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/voa_persian_2003_2008_cleaned.txt))
+ - [Persian poems corpus](https://github.com/amnghd/Persian_poems_corpus): 61MB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/poems_merged.txt))
+ - [TEP: Tehran English-Persian parallel corpus](http://opus.nlpl.eu/TEP.php): 33MB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/TEP_fa.txt))
+ 
 #### AGP
+This corpus was a formerly private corpus for ASR Gooyesh Pardaz which is now published for all users by this project. This corpus contains more than 140 million paragraphs summed up in 23GB (after cleaning). This corpus is a mixture of both formal and informal paragraphs that are crawled from different websites and/or social media.
 
-#### OSCAR-fa
+#### [OSCAR-fa](https://oscar-corpus.com/)
+OSCAR (Abadji et al., 2022) or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the go classy architecture. Data is distributed by language in both original and deduplicated form. We used the unshuffled-deduplicated-fa from this corpus, after cleaning there were about 36GB remaining.
 
 #### Telegram
+Telegram, a cloud-based instant messaging service, is a widely used application in Iran. Following this hypothesis, we prepared a list of Telegram channels in Farsi covering various topics including sports, daily news, jokes, movies and entertainment, etc. The text data extracted from mentioned channels mainly contains informal data.
 
-#### LSCP
+#### [LSCP](https://iasbs.ac.ir/~ansari/lscp/)
+The Large Scale Colloquial Persian Language Understanding dataset has 120M sentences from 27M casual Persian sentences with its derivation tree, part-of-speech tags, sentiment polarity, and translations in English, German, Czech, Italian, and Hindi. However, we just used the Farsi part of it and after cleaning we had 2.3GB of it remaining. Since the dataset is casual, it may help our corpus have more informal sentences although its proportion to formal paragraphs is not comparable.
 
 #### Initial Data Collection and Normalization